An ℋ approach to stability analysis of switched Hopfield neural networks with time-delay

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59 Citations (Scopus)

Abstract

This paper proposes a new ℋ weight learning law for switched Hopfield neural networks with time-delay under parametric uncertainty. For the first time, the ℋ weight learning law is presented to not only guarantee the asymptotical stability of switched Hopfield neural networks, but also reduce the effect of external disturbance to an ℋ norm constraint. An existence condition for the ℋ weight learning law of switched Hopfield neural networks is expressed in terms of strict linear matrix inequality (LMI). Finally, a numerical example is provided to illustrate our results.

Original languageEnglish
Pages (from-to)703-711
Number of pages9
JournalNonlinear Dynamics
Volume60
Issue number4
DOIs
Publication statusPublished - 2010 Jun
Externally publishedYes

Keywords

  • Linear matrix inequality (LMI)
  • Lyapunov-Krasovskii stability theory
  • Switched Hopfield neural networks
  • Weight learning law
  • ℋ stability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering

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